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Scaling up ML-based Black-box Planning with Partial STRIPS Models
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like policy learning.
Exploiting Learned Policies in Focal Search
In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier.
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